Pengaruh Model Pembelajaran Project Based Learning (PjBL) Berbantuan Mind Map Terhadap Kemampuan Berpikir Kreatif Mahasiswa
Bibliographic record
Abstract
This research aims to test whether the use of a project based learning model assisted by mind maps has an effect on students' creative thinking abilities. The research sample consisted of 50 class A and B students using Shapiro Wilk. quantitative method with a Quasi Experimental Design and a Nonequivalent Pretest-Posttest control group design scheme. Data was collected through pretest and posttest to obtain the N-Gain Score value between the experimental class and the control class. with a sampling technique using a purposive sampling method. Data analysis was carried out using parametric statistical tests, namely the Anova Test. Data analysis results Based on the results of data analysis obtained during the research, it shows that the N-gain score for the experimental class got an average score of 80%, categorized as effective in using the project based learning model assisted by mind maps, while the control class got an average score of 21%, categorized as ineffective in using conventional learning. When testing the hypothesis using the ANOVA test, Sig 0.000 ≤ 0.05 was obtained so that there was a significant difference between the experimental class and the control class. Thus Ho is rejected and Ha is accepted. So it can be concluded that there is a significant influence of the PjBL model assisted by mind maps on students' creative thinking abilities.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".